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- import xgboost as xgb
- import numpy as np
- from sklearn.datasets import load_digits
- from sklearn.cross_validation import train_test_split
- rng = np.random.RandomState(1994)
- digits = load_digits(2)
- X = digits['data']
- y = digits['target']
- X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
- dtrain = xgb.DMatrix(X_train, y_train)
- dtest = xgb.DMatrix(X_test, y_test)
- param = {'objective': 'binary:logistic',
- 'tree_method': 'hist',
- 'grow_policy': 'depthwise',
- 'max_depth': 3,
- 'eval_metric': 'auc'}
- res = {}
- bst = xgb.train(param, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')],
- evals_result = res)
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